Embodiments of the present technique relate generally to health monitoring, and more particularly to methods and systems for fall detection.
Unintentional falls are one of the most complex and costly health issues facing elderly people. Recent studies show that approximately one in every three adults aged 65 years or older falls each year, and about 30 percent of these falls result in serious injuries. Particularly, people who experience a fall event at home may remain on the ground for an extended period of time as help may not be immediately available. The studies indicate a high mortality rate amongst people who remain on the ground for an hour or more after a fall.
Fall detection (FD), therefore, has become a major focus of healthcare facilities. Conventionally, healthcare facilities employ nursing staff to monitor a person around the clock. Such care tends to be very costly and requires the nursing staff to be constantly alert. In settings such as assisted living or independent community life, however, the desire for privacy and the associated expense render such constant monitoring undesirable. Accordingly, FD systems based on wearable devices including sensors such as accelerometers, gyroscopes and/or microphones have been proposed. These devices, however, may need to be activated by a fallen person using a push-button to alert appropriate personnel or an associated health monitoring system. The FD systems based on such wearable devices, therefore, may be successful only if the person wears the sensing devices at all times and is physically and cognitively able to activate the alarm when an emergency arises.
Therefore, video-based FD systems are being widely investigated for efficient fall detection. Conventional video-based FD systems use rapid acceleration and impact as an indication of a falling person. Accordingly, the video-based FD systems process images of the person's motion in real time to evaluate if detected horizontal and vertical velocities corresponding to the person's motion indicate a fall event. Determining the horizontal and vertical velocities for detecting human falls in a large area involves use of complex computations, thus requiring a great deal of processing power and expensive equipment. Additionally, such video-based FD systems often fail to detect slow falls, for example, when an elderly person slides out of a bed or chair or otherwise breaks the fall but still finds him or herself on the floor in need of immediate assistance. Further, the video-based FD systems are also unable to detect falls obstructed by the presence of objects such as chairs and tables disposed in the field of view. Moreover, use of such video-based FD systems typically involves acquisition of personally identifiable information leading to numerous privacy concerns. Specifically, constant monitoring and acquisition of identifiable videos is considered by many people to be an intrusion of their privacy.
It is desirable to develop unobtrusive and cost-effective methods and systems for detecting human fall events. Specifically, there is a need for efficient FD systems and methods that non-intrusively yet reliably detect human fall events regardless of whether or not the detected motion includes rapid acceleration and impact. Furthermore, it is desirable to develop systems and methods that do not require line-of-sight and are capable of detecting fall events even in cluttered spaces with a fairly low instance of false alarms.